The Role of PNS Simulation in Gradient Design
1Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States
Synopsis
Peripheral Nervous Stimulation (PNS) has become the major limitation in many fast MRI sequences for state-of-the-art gradient systems. This educational talk provides an overview on how PNS modeling tools can inform the design phase of new gradient systems to maximize the safely usable image encoding performance.
Content
Rapid switching of MRI gradient coils induces electric fields in the human body strong enough to induce peripheral nerve stimulation (PNS)1,2. The occurrence of PNS limits the usable performance of many state-of-the-art gradient systems, leading to longer scan-times or reduced spatial and temporal resolution. Although most prominent in whole-body systems due to the large body area exposed3, PNS is also becoming a limiting factor in head-gradient systems4,5. Despite its impact, PNS metrics are only indirectly addressed during the coil design phase, e.g., by reducing the linear volume6 or by conducting stimulation experiments using healthy human subjects in constructed coil prototypes.In this educational session, we will give an overview on how PNS modeling approaches7,8,9,10 can be used to guide the design phase of novel MRI gradient systems to maximize the safely usable image encoding performance. This includes:
- Basic mechanisms behind PNS in MRI, effect of body geometry and coil current waveform on PNS thresholds
- Overview of PNS modeling approaches, “Electric field” based vs. “Coupled EM-Neurodynamic” based methods
- Basics of Neurodynamic Modeling11,12,13
- Application of PNS modeling in the design optimization phase of a new high-performance head gradient
- Incorporating PNS metrics in the Gold-Standard method for numeric coil winding optimization14,15
- Analyzing the effect of scan position on PNS thresholds in unoptimized and optimized gradient coils
Acknowledgements
No acknowledgement found.References
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[14] Peeren et al., “Stream function approach for determining optimal surface currents”. Journal of Computational Physics, 191(1), 2003
[15] Lemdiasov et al., “A stream function method for gradient coil design”, Concepts Magn. Reson., 26(1), 2005
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